Instructions to use GeneralAnalysis/GA_Guard_1B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use GeneralAnalysis/GA_Guard_1B with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="GeneralAnalysis/GA_Guard_1B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("GeneralAnalysis/GA_Guard_1B")
model = AutoModelForCausalLM.from_pretrained("GeneralAnalysis/GA_Guard_1B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use GeneralAnalysis/GA_Guard_1B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "GeneralAnalysis/GA_Guard_1B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "GeneralAnalysis/GA_Guard_1B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/GeneralAnalysis/GA_Guard_1B

SGLang

How to use GeneralAnalysis/GA_Guard_1B with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "GeneralAnalysis/GA_Guard_1B" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "GeneralAnalysis/GA_Guard_1B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "GeneralAnalysis/GA_Guard_1B" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "GeneralAnalysis/GA_Guard_1B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use GeneralAnalysis/GA_Guard_1B with Docker Model Runner:
```
docker model run hf.co/GeneralAnalysis/GA_Guard_1B
```

rezzzy commited on 4 days ago

Commit

524ea00

verified ·

1 Parent(s): e1872a5

Use GA Guard 1B canonical repo id

Browse files

Files changed (1) hide show

README.md +2 -2

README.md CHANGED Viewed

@@ -55,7 +55,7 @@ The tokenizer chat template bakes in the guard system prompt and automatically p
 import torch
 from transformers import AutoModelForCausalLM, AutoTokenizer
-MODEL_ID = "GeneralAnalysis/ga_guard_llama"
 tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
 model = AutoModelForCausalLM.from_pretrained(
@@ -80,7 +80,7 @@ print(tokenizer.decode(out[0, inputs["input_ids"].shape[1]:], skip_special_token
 from transformers import AutoTokenizer
 from vllm import LLM, SamplingParams
-MODEL_ID = "GeneralAnalysis/ga_guard_llama"
 llm = LLM(model=MODEL_ID, dtype="bfloat16", enable_prefix_caching=True)
 tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)

 import torch
 from transformers import AutoModelForCausalLM, AutoTokenizer
+MODEL_ID = "GeneralAnalysis/GA_Guard_1B"
 tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
 model = AutoModelForCausalLM.from_pretrained(
 from transformers import AutoTokenizer
 from vllm import LLM, SamplingParams
+MODEL_ID = "GeneralAnalysis/GA_Guard_1B"
 llm = LLM(model=MODEL_ID, dtype="bfloat16", enable_prefix_caching=True)
 tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)